Evaluation of different virtual screening programs for docking in a charged binding pocket

Journal of Chemical Information and Modeling

Volumn 48, Issue 10, 2008, Pages 2010-2020

  Deng, Wei ^a   Verlinde, Christophe L M J ^a  

^a UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINDERS; BINDING ENERGY; CELL PROLIFERATION; MOLECULES;

BINDING POCKETS; DOCKING METHODS; DOCKING PROGRAMS; NEGATIVELY CHARGED; PROTEIN TARGETS; SCORING FUNCTIONS; SMALL MOLECULES; VIRTUAL SCREENING;

C (PROGRAMMING LANGUAGE);

CYTOCHROME C OXIDASE; DRUG RECEPTOR; HEME; LIGAND; PROTEIN;

ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; COMPUTER SIMULATION; DRUG SCREENING; EVALUATION; METHODOLOGY; PROTEIN BINDING; PROTEIN CONFORMATION; ROC CURVE; STEREOISOMERISM;

COMPUTER SIMULATION; DRUG EVALUATION, PRECLINICAL; ELECTRON TRANSPORT COMPLEX IV; HEME; LIGANDS; MODELS, MOLECULAR; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEINS; RECEPTORS, DRUG; ROC CURVE; STEREOISOMERISM;

EID: 56449104546     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci800154w     Document Type: Article

References (82)

1. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discovery 2004, 3, 935-949.
2. Shoichet, B. K. Virtual screening of chemical libraries. Nature 2004, 432, 862-865.
3. Abagyan, R.; Totrov, M. High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 2001, 5, 375-382.
4. Alvarez, J. C. High-throughput docking as a source of novel drug leads. Curr. Opin. Chem. Biol. 2004, 8, 365-370.
5. Jenkins, J. L.; Shapiro, R. Identification of small-molecule inhibitors of human angiogenin and characterization of their binding interactions guided by computational docking. Biochemistry 2003, 42, 6674-6687
6. Vangrevelinghe, E.; Zimmermann, K.; Schoepfer, J.; Portmann, R.; Fabbro, D.; Furet, P. Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. J. Med. Chem. 2003, 46, 2656-2662.
7. Jozwiak, K.; Ravichandran, S.; Collins, J. R.; Wainer, I. W. Interaction of noncompetitive inhibitors with an immobilized α3β4 nicotinic acetylcholine receptor investigated by affinity chromatography, quantitative structure-activity relationship analysis, and molecular docking. J. Med. Chem. 2004, 47, 4008-4021.
8. Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langidge, R.; Ferrin, T. E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 1982, 161, 269-288.
9. Brooijmans, N.; Kuntz, I. D. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct. 2003, 32, 335-373.
10. Halperin, I.; Ma, B.; Wolfen, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002, 47, 409-443.
11. Taylor, R. D.; Jewsbury, P. J.; Essex, J. W. A review of protein-small molecule docking methods. J. Comput.-Aided. Mol. Des. 2002, 16, 151-166.
12. McMartin, C.; Bohacek, R. S. QXP: Powerful, rapid computer algorithms for structure-based drug design. J. Comput.-Aided Mol. Des. 1997, 11, 333-344.
13. Totrov, M.; Abagyan, R. Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins 1997, 1, 215-220.
14. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997, 267, 727-748.
15. Baxter, C. A.; Murray, C. W.; Clark, D. E.; Westhead, D. R.; Eldridge, M. D. Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins 1998, 33, 367-382.
16. Morris, G.; Goodsell, D.; Halliday, R.; Huey, R.; Hart, W.; Belew, R.; Olson, A. J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 1998, 19, 1639-1662.
17. Kramer, B.; Rarey, M.; Lengaue, r T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 1999, 37, 228-241.
18. Moustakas, D. T.; Lang, P. T.; Pegg, S.; Pettersen, E.; Kuntz, I. D.; Brooijmans, N.; Rizzo, R. C. Development and validation of a modular, extensible docking program: DOCK 5. J. Comput-Aided Mol. Des. 2006, 20, 601-619.
19. Diller, D. J.; Merz, K. M., Jr. High throughput docking for library design and library prioritization. Proteins 2001, 43, 113-124.
20. David, L.; Luo, R.; Gilson, M. K. ligand-receptor docking with the mining minima optimizer. J. Comput.-Aided Mol. Des. 2001, 15, 157-171.
21. McGann, M. R.; Almond, H. R.; Nicholls, A.; Grant, J. A.; Brown, F. K. Gaussian docking functions. Biopolymers 2003, 68, 76-90.
22. Jain, A. N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 2003, 46, 499-511.
23. Venkatachalam, C. M.; Jiang, X.; Oldfield, T.; Waldman, M. Ligandfit: A novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graphics Modell. 2003, 21, 289-307.
24. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved protein-ligand docking using GOLD. Proteins 2003, 52, 609-623.
25. Taylor, R. D.; Jewsbury, P. J.; Essex, J. W. FDS: Flexible ligand and receptor docking with a continuum solvent model and soft-core energy function. J. Comput. Chem. 2003, 24, 1637-1656.
26. Stahl, M.; Rarey, M. Detailed analysis of scoring functions for virtual screening. J. Med. Chem. 2001, 44 (7), 1035-1042.
27. Schulz-Gasch, T.; Stahl, M. Binding site characteristics in structure-based virtual screening: Evaluation of current docking tools. J. Mol. Model. 2003, 9(1), 47-57.
28. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46 (12), 2287-2303.
29. Perola, E.; Walters, W. P.; Charifson, P. S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins: Struct., Funct., Bioinf. 2004, 56 (2), 235-249.
30. Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins: Struct., Funct., Bioinf. 2004, 57 (2), 225-242.
31. Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation of docking performance: Comparative data on docking algorithms. J. Med. Chem. 2004, 47 (3), 558-565.
32. Xing, L.; Hodgkin, E.; Liu, Q.; Sedlock, D. Evaluation and application of multiple scoring functions for a virtual screening experiment. J. Comput.-Aided Mol. Des. 2004, 18 (5), 333-344.
33. Cummings, M. D.; DesJarlais, R. L.; Gibbs, A. C.; Mohan, V.; Jaeger, E. P. Comparison of automated docking programs as virtual screening tools. J. Med. Chem. 2005, 48, 962-976.
34. Chen, H.; Lyne, P. D.; Giordanetto, F.; Lovell, T.; Li, J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model. 2006, 46 (1), 401-415.
35. Warren, G. L.; Andrews, C. W.; Capelli, A.; Clarke, B.; La Londe, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49 (20), 5912-5931.
36. Gehlhaar, D. K.; Verkhivker, G. M.; Rejto, P. A.; Sherman, C. J.; Fogel, D. B.; Fogel, L. J.; Freer, S. T. Molecular recognition of the inhibitor AG-1343 by HTV-1 protease: Conformationally flexible docking by evolutionary programming. Chem. Biol. 1995, 2, 317-324.
37. Welch, W.; Ruppert, J.; Jain, A. N. Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites. Chem. Biol. 1996, 3, 449-462.
38. Muegge, I.; Martin, Y. C.; Hajduk, P. J.; Fesik, S. W. Evaluation of PMF scoring in docking weak ligands to the FK506 binding protein. J. Med. Chem. 1999, 42, 2498-2503.
39. Bissantz, C.; Folkers, G.; Rognan, D. Protein-based virtual screening of chemical databases: 1. Evaluation of different docking/scoring combinations. J. Med. Chem. 2000, 43, 4759-4767.
40. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol. 2000, 295, 337-356.
41. Pearlman, D. A.; Charifson, P. S. Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system. J. Med. Chem. 2001, 44, 3417-3423.
42. Terp, G. E.; Johansen, B. N.; Christensen, I. T.; Jørgensen, F. S. A new concept for multidimensional selection of ligand conformations (Multiselect) and multidimensional scoring (Multiscore) of protein-ligand binding affinities. J. Med. Chem. 2001, 44, 2333-2343.
43. Buzko, O. V.; Bishop, A. C.; Shokat, K. M. Modified AutoDock for accurate docking of protein kinase inhibitors. J. Comput-Aided Mol. Des. 2002, 16, 113-127.
44. Clark, R. D.; Strizhev, A.; Leonard, J. M.; Blake, J. F.; Matthew, J. B. Consensus scoring for ligand/protein interactions. J. Mol. Graphics Model. 2002, 20, 281-295.
45. Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem., Int. Ed. 2002, 41, 2644-2676.
46. eHiTS, version 6.2; SimBioSys Inc.: Toronto ON, Canada, 2008.
47. Laederach, A.; Reilly, P. J. Modeling protein recognition of carbohydrates. Proteins 2005, 60, 591-597.
48. Andersson, C. D.; Thysell, E.; Lindström, A.; Bylesjö, M.; Raubacher, F.; Linusson, A. A multivariate approach to investigate docking parameters' effects on docking performance. J. Chem. Inf. Model. 2007, 47, 1673-1687.
49. Brenk, R.; Vetter, S. W.; Boyce, S. E.; Goodin, D. B.; Shoichet, B. K. Probing molecular docking in a charged model binding site. J. Mol. Biol. 2006, 357, 1449-1470.
50. Wei, B. Q.; Baase, W. A.; Weaver, L. H.; Matthews, B. W.; Shoichet, B. K. A model binding site for testing scoring functions in molecular docking. J. Mol. Biol. 2002, 322, 339-355.
51. Lorber, D. M.; Shoichet, B. K. Hierarchical docking of databases of multiple ligand conformations. Curr. Top. Med. Chem. 2005, 5, 739-749.
52. FRED, version 2.2.1; Openeye Scientific Software Inc.: Santa Fe, NM, 2007.
53. QXP/FLO, version 99.6; ThistleSoft Inc.: Colebrook, CT, 1999.
54. GLIDE, version 3.5; Schrodiner Inc.: Portland, OR, 2008.
55. Eriksson, A. E.; Baase, W. A.; Wozniak, J. A.; Matthews, B. W. A cavity-containing mutant of T4 lysozyme is stabilized by buried benzene. Nature 1992, 355, 371-373.
56. Morton, A.; Baase, W. A.; Matthews, B. W. Energetic origins of specificity of ligand binding in an interior nonpolar cavity of T4 lysozyme. Biochemistry 1995, 34, 8564-8575.
57. Fitzgerald, M. M.; Churchill, M. J.; McRee, D. E.; Goodin, D. B. Small molecule binding to an artificially created cavity at the active site of cytochrome c peroxidase. Biochemistry 1994, 33, 3807-3818.
58. Shoichet Laboratory Homepage. University of California, San Francisco: San Francisco, CA. http://shoichetlab.compbio.ucsf.edu/ (accessed Apr 19, 2007), Under "Take Aways → CCP binders".
59. Shoichet Laboratory Homepage. University of California, San Francisco: San Francisco, CA. http://shoichetlab.compbio.ucsf.edu/ (accessed Apr 19, 2007), Under "Take Aways → CCP nonbinders".
60. Banba, S.; Guo, Z. Y.; Brooks, C. L. Efficient sampling of ligand orientations and conformations in free energy calculations using the λ-dynamics method. J. Phys. Chem., Ser. B 2000, 104, 6903-6910.
61. Banba, S.; Brooks, C. L. Free energy screening of small ligands binding to an artificial protein cavity. J. Chem. Phys. 2000, 113, 3423-3433.
62. Rosenfeld, R. J.; Goodsell, D. S.; Musah, R. A.; Morris, G. M.; Goodin, D. B.; Olson, A. J. Automated docking of ligands to an artificial active site: Augmenting crystallographic analysis with computer modeling. J. Comput-Aided Mol. Des. 2003, 17, 525-536.
63. Boström, J. Reproducing the conformations of protein-bound ligands: A critical evaluation of several popular conformational searching tools. J. Comput-Aided Mol. Des. 2001, 15, 1137-1152.
64. Boström, J.; Greenwood, J. R.; Gottfries, J. Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J. Mol. Graphics Modell. 2003, 21, 449-462.
65. FRED user manual, version 2.2, Openeye Scientific Software Inc.: Santa Fe, NM, 2007.
66. Grant, J. A.; Pickup, B. T.; Nicholls, A. A smooth permittivity function for Poisson-Boltzmann solvation methods. J. Comput. Chem. 2001, 22 (6), 608-640.
67. Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996, 118, 11225-11236.
68. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med Chem. 2004, 47, 1739-1749.
69. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759.
70. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra precision Glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem. 2006, 49, 6177-6196.
71. RCSB Protein Data Bank. http://http://www.rcsb.org (accessed Apr 2, 2007).
72. MOE, version 200608; Chemical Computing Group Inc.: Montreal, Quebec, Canada, 2006.
73. Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC Model:II. Parameterization and validation. J. Comput. Chem. 2002, 23, 1623-1641.
74. OMEGA, version 2.2.0; Openeye Scientific Software Inc.: Santa Fe, NM, 2007.
75. Molprobity. http://molprobity.biochem.duke.edu/ (accessed Jun 14, 2007).
76. Chimera, version 1.2422; University of California: San Francisco, CA, 2007; http://www.cgl.ucsf.edu/chimera/.
77. FRED_receptor, version 2.2.1; Openeye Scientific Software Inc.: Santa Fe, NM, 2007.
78. Maestro, version 7.0; Schrodiner Inc.: Portland, OR, 2008.
79. LigPrep, version 16016; Schrodiner Inc.: Portland, OR, 2008.
80. Hand, D.; Mannila, H.; Smyth, P. Principles of Data Mining; The MIT Press: Cambridge, MA, 2001.
81. Teramoto, R.; Fukunishi, H. Supervised consensus scoring for docking and virtual screening. J. Chem. Inf. Model. 2007, 47, 526-534.
82. Marvin, version 5.0.6; ChemAxon Kft: Budapest, Hungary, 2008; http://www.chemaxon.com.